350,907 research outputs found

    The GRaPPa Lab: Supporting Team Decision Making in Complex Environments

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    poster abstractThe GRaPPa (Group Psychology and Performance) Lab operates within the School of Informatics at Indiana University Purdue University Indianapolis (IUPUI), in cooperation with the User Simulation and Experience Research Lab. The focus of our research is on interdependent teams in technologically complex work environments characterized by uncertainty, stress, high risk, changing moods, and varying levels of expertise. The GRaPPa Lab employs a mixed-methodological approach. Field studies provide rich and nuanced knowledge about individuals and teams at work in complex environments. Likewise, controlled laboratory experiments have provided the foundation for countless contributions to our understanding of the human characteristics that impact the development and use of systems, devices, and environments. Yet such experiments are limited in what they can tell us about work situated in real-world settings, just as field studies are limited in their support for precision and replicability. The GRaPPa Lab leverages the strengths of both through the use of simulated task environments and scaled worlds in the search for holistic assessments of group behavior and task performance. This poster will showcase aspects of an ongoing research program, Bridging the Situation Space to Decision Space Gap. This project is examining the modeling and visualization of decision space information to supplement situation space information in the contexts of disease contagion and emergency management. To enhance the decision support of emergency responders, we are examining the ability of decision space visualization tools to enhance option awareness and support more robust decision making. This work is focused on detailing the impact of the decision space information provided to users, relating the correctness of decisions to the levels of complexity represented in the events, and the affordances for understanding alternative actions. This ongoing project is focused on prototyping multiple visualization methods and testing them in human-in-the-loop experiments based on the domain of emergency crisis management. In addition, the computer models underlying the decision space are being expanded to support increasingly complex situations. This research provides further insight into the value of decision space information and option awareness for users working in complex environments

    D-Side: A Facility and Workforce Planning Group Multi-criteria Decision Support System for Johnson Space Center

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    "To understand and protect our home planet, to explore the universe and search for life, and to inspire the next generation of explorers" is NASA's mission. The Systems Management Office at Johnson Space Center (JSC) is searching for methods to effectively manage the Center's resources to meet NASA's mission. D-Side is a group multi-criteria decision support system (GMDSS) developed to support facility decisions at JSC. D-Side uses a series of sequential and structured processes to plot facilities in a three-dimensional (3-D) graph on the basis of each facility alignment with NASA's mission and goals, the extent to which other facilities are dependent on the facility, and the dollar value of capital investments that have been postponed at the facility relative to the facility replacement value. A similarity factor rank orders facilities based on their Euclidean distance from Ideal and Nadir points. These similarity factors are then used to allocate capital improvement resources across facilities. We also present a parallel model that can be used to support decisions concerning allocation of human resources investments across workforce units. Finally, we present results from a pilot study where 12 experienced facility managers from NASA used D-Side and the organization's current approach to rank order and allocate funds for capital improvement across 20 facilities. Users evaluated D-Side favorably in terms of ease of use, the quality of the decision-making process, decision quality, and overall value-added. Their evaluations of D-Side were significantly more favorable than their evaluations of the current approach. Keywords: NASA, Multi-Criteria Decision Making, Decision Support System, AHP, Euclidean Distance, 3-D Modeling, Facility Planning, Workforce Planning

    Automated user modeling for personalized digital libraries

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    Digital libraries (DL) have become one of the most typical ways of accessing any kind of digitalized information. Due to this key role, users welcome any improvements on the services they receive from digital libraries. One trend used to improve digital services is through personalization. Up to now, the most common approach for personalization in digital libraries has been user-driven. Nevertheless, the design of efficient personalized services has to be done, at least in part, in an automatic way. In this context, machine learning techniques automate the process of constructing user models. This paper proposes a new approach to construct digital libraries that satisfy userā€™s necessity for information: Adaptive Digital Libraries, libraries that automatically learn user preferences and goals and personalize their interaction using this information

    A model-driven method for the systematic literature review of qualitative empirical research

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    This paper explores a model-driven method for systematic literature reviews (SLRs), for use where the empirical studies found in the literature search are based on qualitative research. SLRs are an important component of the evidence-based practice (EBP) paradigm, which is receiving increasing attention in information systems (IS) but has not yet been widely-adopted. We illustrate the model-driven approach to SLRs via an example focused on the use of BPMN (Business Process Modelling Notation) in organizations. We discuss in detail the process followed in using the model-driven SLR method, and show how it is based on a hermeneutic cycle of reading and interpreting, in order to develop and refine a model which synthesizes the research findings of previous qualitative studies. This study can serve as an exemplar for other researchers wishing to carry out model-driven SLRs. We conclude with our reflections on the method and some suggestions for further researc
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